Implementing Rule-based Healthcare Edits

doi:10.3837/tiis.2022.01.007

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical diagnosis applications often use AI-RBSs with medical image processing to select surgical strategies and for other medical tasks [50][51][52][53][54][55]. In reviewing applications of AI-RBS in health systems, both Matsuyama [56] and Abdullah et al [57] have concluded that this way of organizing knowledge can increase the flexibility of software systems and that it is more efficient than conventional approaches, in some cases increasing the confidence in the value of the results to 95%. al Fryan et al [54] recommend using these technologies in other fields of study due to the efficiency of the processes.…”

Section: Introductionmentioning

confidence: 99%

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

Nobre,

McDill,

Estraviz Rodriguez

et al. 2023

Forests

View full text Add to dashboard Cite

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen’s flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper.

show abstract

Section: Introductionmentioning

confidence: 99%

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

Nobre,

McDill,

Estraviz Rodriguez

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

Nobre¹,

McDill²,

Rodriguez³

et al. 2023

Preprint

View full text Add to dashboard Cite

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 60s have been adapted and improved upon over the years. Generating management alternatives for forest management planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. Climate change has made forest management calculations even more complex, requiring flexibility, diverse parameters, models, and methods. Despite this complexity, consistent concepts can be applied in developing management alternatives for forest management planning. This work describes iGen, a flexible forest prescription generator that applies the AI technique Rule-Based System (AI-RBS). iGen projects the state and associated inputs and outputs for a set of management units using rules from its knowledge base. An Inference Engine uses the rules to simulate a set of prescriptions in a tree-like graph structure. Without needing IT specialists, forest managers can describe the potential development of their forest through variables, rules, formulas, functions, and procedures. A key feature of iGen is that it is not limited to, adapted to, or focused on any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. This article introduces iGen, explaining its concepts, structure, and algorithms through two FEM problems: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/…/iGen.

show abstract

Implementing Rule-based Healthcare Edits

Cited by 2 publications

References 18 publications

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

Contact Info

Product

Resources

About